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A network was constructed where two words were connected only if the cosine similarity between their vectors exceeded a threshold. The cosine similarity between a pair of vectors provided a measure of how similar the pair of words were in meaning Mikolov et al. The optimisation of this methodology followed Burnie and Yilmaz a. Mono-phase concepts were more than three-fold higher in popularity Grimes and Schulz, across the phase after the all time high price compared with the phase before, and increased in frequency before the shift in phase.

To determine if frequency rose before the shift, we examined 1, 2, 3 h, and so on, up to 24 h before the shift and evaluated whether the proportion of submissions containing the concept within any of these windows was higher compared with all the submissions in the same phase but before that window.

Words more frequent as prices rose were split from those more popular as prices fell. As in section 2. Rising-price, multi-phase concepts were rising-price concepts that rose in frequency with every shift to rising prices and within the 24 h before every shift to rising prices. Falling-price, multi-phase concepts were falling-price concepts that rose in frequency with every shift to falling prices and within the 24 h before every shift to falling prices.

We removed from the analysis any concept that consistently rose in popularity across every shift in price, independent of whether prices were rising or falling, as any rise in popularity could have been an artefact of the long-term trend.

For each mono-phase and multi-phase concept, we found the top five most common words occurring in submissions containing at least one word from that concept. This excluded words that did not aid in the interpretation of the concept. Further details and a list of words excluded are available in section 1. Both the bitcoin and the ether price rose to an all time high as became , to then oscillate with an overall decline in value until mid-December see Figure 1.

There was a disparity in the timing of the all time high price for bitcoin 16 December and ether 13 January It appears that different price levels acted as barriers at different times. Whilst bitcoin prices rose across , ether prices reverted upon nearing US Dollars Bamburic, ; Wilmoth, 12 June and 1 September , only increasing above this level after five months. Whilst ether prices fell from 5 May to mid-December , bitcoin prices recovered upon falling to 6, US Dollars Cuthbertson, 29 June and 14 August and only fell below this level after four months.

Based on local extrema see Figure S1 and price barriers, we demarcated six phases of price movement with ether and eight with bitcoin see Table 2. Table 2 further shows which of these phases were used in order to compare daily word frequencies so as to filter words see sections 2. Descriptive statistics for the different phases are provided in Table S7. Table 2. For each phase in the cryptocurrency price series: the date range, price movement, overall percentage increase and in which Wilcoxon Rank-Sum Test that phase was used.

Ten mono-phase concepts rose more than three-fold with this shift to falling prices and increased within the 24 h period before entering the falling price phase see Figure 3. Figure 3. Frequency data for mono-phase concepts in the case of Bitcoin. This shows the percentage of all submissions containing the concept in phase 1 light green and phase 2 blue. Table 3. Top five words occurring with each Bitcoin mono-phase concept in phase 2.

Three of these six cryptocurrencies increased more than three-fold in the proportion of submissions from phase 1 to 2: Cardano rose Further details in Table 4. Table 4. Interest in Binance rose Further details in Table 5. Table 5. Table 6. Top five words occurring with each Bitcoin falling-price, multi-phase concept in phases 2, 4, 6, and 8. With Ethereum, ten multi-phase concepts were identified. Further details in Table 7. Table 7. Top five words occurring with each Ethereum rising-price, multi-phase concept in phases 1, 3, and 5.

The remaining seven multi-phase concepts related to falling ether prices. Further details in Table 8. Table 8. Top five words occurring with each Ethereum falling-price, multi-phase concept in phases 4 and 6. The Supplementary Results, in the Supplementary Data Sheet 1 provide further detail on the percentage change in popularity for Bitcoin multi-phase concepts see Table S8 and Ethereum multi-phase concepts Table S9. Of the Bitcoin mono-phase themes see Table 3 , regulatory bans are the closest to capturing a specific external event.

On 16 December , when prices changed to falling, South Korean news media reported how North Korea was using hacks of South Korean exchanges to fund its regime, encouraging South Korean support for a ban Harper, This could have triggered South Koreans to sell bitcoin holdings before this became illegal and possibly even criminal Jaewon, Since approximately a fifth of bitcoin transactions were in South Korean Won at the time Jaewon, , it is coherent with known events that this caused the shift from rising to falling prices.

The remaining Bitcoin mono-phase concepts could be reflections of a change in mind-set among bitcoin-holders prior to selling. All the concepts delineated for Ethereum were multi-phase, having a recurring impact on price over time. This suggests a source of ether price volatility was traders analysing the ether price and comparing it with bitcoin before buying or selling ether.

This was discussed in the context of price as well as buying, trading, and selling see Tables 6 , 8. This supports the widespread influence of technical traders who use just price information to make trading decisions on cryptocurrency price series and is consistent with evidence for price barriers at US Dollars for ether and 6, US Dollars for bitcoin see Figure 1. Including contextual analysis in the framework has shown that some multi-phase concepts were polysemic—being used in a different context in different price phases.

In some cases, this could be because the concept is an artefact of distinct themes of discussion each happening to include the polysemic concept. Our framework identifies plausible causes of the shifts in ether and bitcoin price trends. Approaches from healthcare epidemiology are deployed that facilitate this move from simply observing how word Burnie and Yilmaz, b or topic Burnie and Yilmaz, a interest changed across phases in price to identifying the potential causes of these phasic shifts.

We identify a one-off effect of regulatory bans on bitcoin, a repeated effect of rival innovations on ether and the influence of technical traders, captured through market price discourse, on both cryptocurrencies. Previous social media analyses typically required judgement on which metric was most suitable in extracting insights from the social media text.

For instance, this pre-selected metric could be a measure of sentiment or be based on a topic modelling algorithm. It was only after the values of the metric had been found that the price data were considered, in testing the association between changes in the metric and price Kaminski, ; Garcia and Schweitzer, ; Georgoula et al.

We move from causal inference, where judgement is required to pre-select which potential causes and what causal mechanism should be tested Runge et al. This enables the discovery of new potential causes of price variation which may not have otherwise been considered for testing. None of the potential causes identified innovation, regulatory bans and technical traders were suggested by Kim et al.

The approach of Kim et al. The risk that a concept was spurious was reduced by examining the words within the concept and the words used with that concept, and considering their coherence with known facts see section 3. Hence, contextual analysis suggests that discussions of exchanges were more plausibly a response to fears over bitcoin price leading to discussion of how best to dispose of bitcoin, rather than a primary cause of falling prices.

Multi-phase concepts may have implications for predictive analysis, since these concepts have a predictive association with price that persists across time. Multi-phase concepts may provide an improvement on sentiment metrics such as VADER that have found social media posts to be positive even during falling prices Abraham et al.

However, the other multi-phase concepts differed, suggesting that different predictors may be suitable for different cryptocurrencies. Future work could examine whether black swan events can be found in cryptocurrencies other than Bitcoin and whether such events are shared or unique to a specific cryptocurrency. Better understanding of the causes of shifts between price phases will help investors in diversifying their cryptocurrency investments to reduce risk.

The sources of data are listed in section 2. The code used to prepare and analyse this data is publicly accessible in a Dryad data repository Burnie et al. AB processed and analysed the data, and drafted the article. EY and TA provided critical feedback on the article, inputting on the data processing and analysis approaches taken. All authors gave final approval for publication and agree to be held accountable for the work performed therein. The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Abraham, J. Cryptocurrency price prediction using tweet volumes and sentiment analysis. SMU Data Sci. Google Scholar. Aggarwal, R. Psychological barriers in gold prices? Alvarez, J. Crypto profiles: vitalik buterin, creator of ethereum. Bamburic, M. Baumgartner, J. Pushshift API.

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Yahoo Finance. ConsenSys Media Cornfield, J. Smoking and lung cancer: recent evidence and a discussion of some questions. Cancer Inst. Cuthbertson, A. The Independent. Dablander, F. Node centrality measures are a poor substitute for causal inference. Dowling, M. Psychological barriers in oil futures markets. Energy Econ. ElBahrawy, A. Wikipedia and cryptocurrencies: interplay between collective attention and market performance.

Blockchain Etherscan Ethereum Ether Historical Prices. Frecknall-Hughes, J. Why the UK tax year begins on April 6 it's a very strange tale. Garcia, D. Social signals and algorithmic trading of Bitcoin. The digital traces of bubbles: feedback cycles between socio-economic signals in the Bitcoin economy.

Georgoula, I. Using time-series and sentiment analysis to detect the determinants of bitcoin prices. Granger, C. Testing for Causality. Control 2, — Grimes, D. Bias and causal associations in observational research. Lancet , — Harding, D. Harper, C. The Merkle Hash. Ioannidis, J. Exposure-wide epidemiology: revisiting Bradford Hill. Jaewon, K.

South Korea joins in Asia-wide bitcoin crackdown. Nikkei Asian Review. Kagan, J. Tax year definition. Kaminski, J. No miner downtime 1 year contract duration No maintenance fee Daily payout In stock. Hashing power for Ethereum mining can also be easily rented on NiceHash. Ethereum miners can analyse the Ether mining profitability using calculators.

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